| """Сверка НАШЕЙ проприоцепции с распределением, на котором обучался чекпойнт. |
| |
| Гоняем реальную политику несколько эпизодов, копим состояния ДО нормировки и |
| сравниваем поканально с norm_stats чекпойнта. Канал, уходящий далеко за |
| [q01, q99], модель видит как экстремальный выброс — целиться ей нечем. |
| """ |
| import json, os, sys, glob |
| import numpy as np |
| import torch |
|
|
| os.environ.setdefault("MUJOCO_GL", "egl") |
| import simpler_env |
| from simpler_env.utils.env.observation_utils import get_image_from_maniskill2_obs_dict |
| from scipy.spatial.transform import Rotation |
| from transforms3d.euler import euler2axangle |
| from lerobot.common.policies.factory import load_pretrained_policy |
| from lerobot.common.utils.torch_observation import ( |
| move_dict_to_batch_for_inference, torch_preprocess_dict_inference) |
|
|
| CKPT = sys.argv[1] if len(sys.argv) > 1 else "SberRoboticsCenter/GreenVLA-5b-stride-1-R1-bridge" |
| NEP = int(sys.argv[2]) if len(sys.argv) > 2 else 4 |
|
|
| policy, itf, otf = load_pretrained_policy(CKPT, data_config_name="bridge") |
| policy.to("cuda:0").eval() |
|
|
| from lerobot.common.policies.factory import _resolve_norm_stats_dir |
| import pathlib |
| ns = None |
| _d = pathlib.Path(_resolve_norm_stats_dir(CKPT, "bridge")) / "bridge" / "norm_stats.json" |
| if _d.exists(): |
| ns = json.load(open(_d)) |
| print("norm_stats: %s" % _d) |
| else: |
| print("norm_stats НЕ НАЙДЕН по %s" % _d) |
|
|
|
|
| def build_state(env, base_frame=True): |
| agent = env.unwrapped.agent |
| link = [l for l in agent.robot.get_links() if l.get_name() == "ee_gripper_link"][0] |
| pose = link.get_pose() |
| if base_frame: |
| pose = agent.robot.get_root_pose().inv() * pose |
| q = pose.q |
| rpy = Rotation.from_quat([q[1], q[2], q[3], q[0]]).as_euler("xyz") |
| g = float(agent.get_gripper_closedness()) |
| return np.array([pose.p[0], pose.p[1], pose.p[2], |
| rpy[0], rpy[1], rpy[2], 0.0, 1.0 - g], np.float32) |
|
|
|
|
| states, acts = [], [] |
| for ep in range(NEP): |
| env = simpler_env.make("widowx_spoon_on_towel") |
| obs, _ = env.reset(options={"obj_init_options": {"episode_id": ep}}) |
| prompt = env.get_language_instruction() |
| if hasattr(policy, "reset"): |
| policy.reset() |
| for step in range(40): |
| st = build_state(env) |
| states.append(st) |
| raw = {"observation/state": st, |
| "observation/image": get_image_from_maniskill2_obs_dict(env, obs), |
| "prompt": prompt} |
| with torch.inference_mode(): |
| batch = move_dict_to_batch_for_inference( |
| torch_preprocess_dict_inference(itf(raw)), device="cuda:0") |
| a = policy.select_action(batch).cpu().numpy() |
| a = np.asarray(otf({"actions": a, |
| "state": batch["state"].cpu().numpy()})["actions"]).reshape(-1, 7) |
| acts.append(a[0]) |
| act = a[0].astype(np.float64).copy() |
| axis, ang = euler2axangle(act[3], act[4], act[5]) |
| act[3:6] = axis * ang |
| act[6] = 2.0 * (act[6] > 0.5) - 1.0 |
| obs, r, term, trunc, info = env.step(act) |
| if term or trunc: |
| break |
| env.close() |
|
|
| S = np.array(states) |
| A = np.array(acts) |
| names = ["x", "y", "z", "roll", "pitch", "yaw", "gripper"] |
| mask = [0, 1, 2, 3, 4, 5, 7] |
| S7 = S[:, mask] |
| print("") |
| print("состояний собрано: %d" % len(S7)) |
|
|
| if ns is not None: |
| print("ключи norm_stats: %s" % sorted(ns.keys())) |
| ns = ns.get("norm_stats", ns) |
| print("вложенные ключи: %s" % sorted(ns.keys())) |
| st = ns.get("state") or {} |
| print("поля state: %s" % sorted(st.keys())) |
| q01 = np.array(st.get("q01", []), float) |
| q99 = np.array(st.get("q99", []), float) |
| mean = np.array(st.get("mean", []), float) |
| std = np.array(st.get("std", []), float) |
| hdr = "%-9s%10s%10s%10s%10s %s" % ("канал", "наш min", "наш max", "q01", "q99", "вердикт") |
| print(hdr) |
| for i, n in enumerate(names): |
| lo, hi = float(S7[:, i].min()), float(S7[:, i].max()) |
| if i < len(q01): |
| a_, b_ = float(q01[i]), float(q99[i]) |
| rng = max(b_ - a_, 1e-6) |
| out = max((a_ - lo) / rng, (hi - b_) / rng, 0.0) |
| verdict = "ок" if out < 0.25 else ("ВНЕ ДИАПАЗОНА на %.1f ширины" % out) |
| print("%-9s%10.3f%10.3f%10.3f%10.3f %s" % (n, lo, hi, a_, b_, verdict)) |
| elif i < len(mean): |
| print("%-9s%10.3f%10.3f%10.3f%10.3f (mean/std)" % (n, lo, hi, float(mean[i]), float(std[i]))) |
|
|
| print("") |
| print("действия после денормализации, поканально min/max:") |
| for i, n in enumerate(names): |
| print(" %-8s%9.4f%9.4f" % (n, float(A[:, i].min()), float(A[:, i].max()))) |
|
|
